Translator Disclaimer
14 January 1999 Machine vision algorithm generation using human visual models
Author Affiliations +
Proceedings Volume 3543, Precision Agriculture and Biological Quality; (1999)
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States
The design of robust machine vision algorithms is one of the most difficult parts of developing and integrating automated systems. Historically, most of the techniques have been developed using ad hoc methodologies. This problem is more severe in the area of natural/biological products. In this arena, it has been difficult to capture and model the natural variability to be expected in the products. This present difficulty in performing quality and process control in the meat, fruit and vegetable industries. While some systems have been introduced, they do not adequately address the wide range of needs. This paper will propose an algorithm development technique that utilizes modes of the human visual system. It will address that subset of problems that humans perform well, but have proven difficult to automate with the standard machine vision techniques. The basis of the technique evaluation will be the Georgia Tech Vision model. This approach demonstrates a high level of accuracy in its ability to solve difficult problems. This paper will present the approach, the result, and possibilities for implementation.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wayne D. Daley, Theodore J. Doll, Shane W. McWhorter, and Anthony A. Wasilewski "Machine vision algorithm generation using human visual models", Proc. SPIE 3543, Precision Agriculture and Biological Quality, (14 January 1999);


Symmetry detection of 2-D figures
Proceedings of SPIE (January 01 1990)
Preference for art: similarity, statistics, and selling price
Proceedings of SPIE (February 17 2010)
Perceptual Analysis Of Sampled Color Monitor Displays
Proceedings of SPIE (June 24 1988)

Back to Top